Implement debug.trace[B] : B that prints out an argument that passes
through it.
Visualize colorfully aligned list of vertices

Vertices are checked in turn against dispersion if it belongs the same
cluster. Cluster are colored into different tinge.
Implement clearMinors and clearRandomly

The clearMinors method purges vector from the less variative axes to 
restrict dimension of vector. The clearRandomly is acting the same
wiping out random axes.
Purge nodes with clearMinors()
Nothing much
Nothing much
Use as words integer ids instead of strings.
Nothing much
Iterate through pairs (key, value)
Iterate through pairs (key, value)
Introduce an empty tree :) to simplify initialization
Remove a few asInstanceOf
Generate four random  centroids
Nothing much
Implement both of norm() and sqr() methods
Compute full energy for all of the subtrees

A sum of energies of subtrees plus their mutual energy assigned to
energy of their parent node
Calculate something dispersion-like
Colorify cluster by means of energy estimation
Implement a tool to discern clusters
Consider distance as a deflection from average

In this case dispersion is a sum of squares of distances from
hypothetical average vector that should be the case if it's a cluster,
that should be truth by construction of algorithm.